Data Science Projects for Freelancers

I need a complete, end-to-end pipeline that starts with a screenshot of a live trading terminal, pulls every ticker, quote, or indicator visible on the image, and ends with a single buy-or-sell probability score that consistently reaches 90 %+ confidence.

Scope
• Screenshot ingestion and parsing: an OCR/visual-recognition routine that can handle typical broker or data screens, isolate each symbol, price, volume and time stamp, and structure it as tabular data.
• Feature engineering: enrich the extracted snapshot with any historical data you deem necessary (you may tap your own CSV archives or free APIs), then create derived variables such as intraday return, volatility, or technical signals.
• Probabilistic modeling: build and train a machine-learning model—choice of algorithm is open, as long as it is explainable and achieves the target accuracy on held-out data. Neural networks, tree ensembles or hybrid methods are all acceptable if they outperform simpler baselines.
• Scoring interface: a lightweight CLI or web endpoint that accepts a new screenshot and returns a JSON payload containing “BUY”, “SELL” and the associated probability.
• Documentation & reproducibility: clean, commented Python code (TensorFlow, PyTorch, scikit-learn or similar), requirements.txt, and a short read-me describing set-up and retraining steps.

Acceptance criteria
1. End-to-end demo with two unseen screenshots producing valid scores.
2. Back-test report showing at least 90 % directional accuracy across 1,000+ historical samples.
3. Code passes linting and runs on a fresh environment with the provided instructions.

If parts of the stack—OCR, data augmentation, or model selection—require iterations, please outline your milestone plan so we can agree on checkpoints and validation tests before moving forward.

Skills: Python, Matlab and Mathematica, Software Architecture, Machine Learning (ML), C++ Programming, Data Science, Neural Networks, Deep Learning, Livestock

Hi Pulkit sir
​I am looking for an experienced Quantitative / Algorithmic Trading Developer to build a fully automated, low-latency custom trading setup in Python. The strategy is strictly based on Market Depth (Level 2 Order Book Data) and aims to capture intraday trend reversals in high-volume stocks (specifically targeting the Nifty Pharma sector, such as Sun Pharma, Dr. Reddy's, Zydus, Lupin, etc.).
​Here are the core technical requirements for the project:
​Data Ingestion (Low Latency):
​Implement a robust WebSocket connection using Python's asyncio and websockets libraries to stream real-time Level 2 Market Depth (Top 5 Bids and Asks with exact quantities) directly from the broker (Fyers API / Dhan HQ API).
​The listener should be highly stable and capable of handling fast data ticks during peak market hours without lagging.
​Core Logic Engine (Liquidity & Order Flow Analytics):
​The algorithm needs to constantly monitor a specific watchlist of stocks near key intraday structural levels.
​Signal Trigger: Identify Price Action Reversals via Order Book Absorption. The script should detect when price stalls at a resistance/support zone despite high aggressive volume, signaling that institutional limit orders ("Big Walls") are absorbing the flow.
​Confirmation: Combine order book depth analysis (e.g., Bid-Ask volume imbalance ratio shifting significantly) with secondary technical confirmations like a sharp Volume Spike or RSI divergence.
​Risk Management & Order Execution:
​Instant order punching via the broker's Trading API using market or immediate limit orders once conditions are met.
​Automated Stop-Loss placement dynamically behind the "Big Wall" price level or structural high/low, along with predefined target tracking.
​Notifications & Logs:
​Integration with the Telegram API to broadcast real-time operational logs, signal generation details, and execution statuses (e.g., "Signal detected, Order placed for Sun Pharma at ₹XXXX").
​Deployment:
​The code should be clean, modular, well-documented, and optimized to run 24/7 on a Linux VPS (Ubuntu server) with proper exception handling and auto-reconnect logic if the broker's API drops.
​What I am looking for in a Developer:
​Prior hands-on experience working with Indian Broker APIs (Fyers, Dhan, etc.), specifically handling multi-level WebSocket depth feeds.
​Strong background in writing asynchronous Python code (asyncio) for financial markets.
​Experience in deploying trading scripts on cloud structures (AWS/DigitalOcean VPS).
​Please let me know your previous experience with Level 2/Order flow data and how we can take this forward. Looking forward to your proposal!

We are seeking a detail-oriented Virtual Data Analyst to join our team. The ideal candidate will have experience in data analysis and be proficient in Microsoft Excel. Responsibilities include analyzing data, creating reports, and providing insights to support business decisions. This is a part-time role with a long-term engagement.

Remote · Freelance · Part-time/Consulting

Summary:

We are a growing engineering consultancy working with production-grade AI and data systems. We are expanding our expert network and looking for seasoned engineers who enjoy working on complex, real-world infrastructure — not greenfield toy projects. If you have built and operated ML platforms, data pipelines, or cloud-native analytics systems at scale, we want to talk.

Responsibilities:

· Design and operate cloud-native data platforms on AWS and/or GCP in production environments.
· Build and maintain batch and real-time ETL/ELT pipelines using Airflow, Spark, Kafka, dbt, and Flink.
· Develop and operate MLOps pipelines: model versioning, drift detection, auto-scaling, and continuous training.
· Deploy and monitor machine learning models in real-time and batch environments.
· Implement platform observability: Prometheus, Grafana, OpenTelemetry, CloudWatch.
· Guide clients through platform decisions, migrations, and cost optimizations.
· Deliver clear, actionable recommendations and engineering best practices.

Requirements:

· Proven production experience with ML infrastructure or data platforms at scale.
· Hands-on expertise with AWS (SageMaker, EMR, ECS/EKS, Step Functions) or GCP (BigQuery, Vertex AI, GKE).
· Strong command of MLOps tooling: MLflow, Kubeflow, TFX, or SageMaker Pipelines.
· Experience with data engineering frameworks: Airflow, Spark, Kafka, dbt, and Delta Lake / Iceberg.
· Proficiency in Python, SQL, and at least one of: Go, Bash, Java
Familiarity with Kubernetes, Docker, Helm, and IaC tools (Terraform, CloudFormation).
· A reliability-first mindset with a structured approach to troubleshooting.
· Strong written and spoken English — you will communicate directly with clients.

Engagement Details:

This is a part-time, consulting-based role with flexible hours. Ongoing or project-based depending on client needs. Well suited for senior data or ML engineers who prefer working on live, complex systems. We serve clients across FinTech, Healthcare, Retail, and SaaS.

To Apply:

Include a brief note describing an ML or data platform you have supported in production: approximate scale, architecture, your specific role, and any significant incidents or migrations you have handled. Generic applications without concrete examples will not be reviewed.

I am seeking an experienced technical financial writer to produce a high-quality, deeply detailed case study and technical article (approximately 5,000–8,000 words) documenting a portfolio optimization platform I have built.The article will explain how the platform integrates traditional financial methods with advanced machine learning enhancements to outperform standard benchmarks (like the S&P 500 index and equal-weight portfolios). The final piece should be structured like a top-tier industry whitepaper or an in-depth, multi-part technical series suitable for platforms like Medium (Towards Data Science), Substack, or a corporate engineering blog.Required QualificationsFinancial Content Experience: A strong background writing about quantitative finance, portfolio optimization, or fintech. Please provide 2–3 relevant writing samples or technical reports.Domain Knowledge: A clear understanding of modern portfolio theory, risk metrics (Sharpe, Sortino, Calmar, VaR, CVaR), and data pipelines (yfinance, lookback windows, SLSQP solvers, Ledoit-Wolf covariance estimation).Code Literacy: Ability to read and understand Python code (specifically a Streamlit application framework) to accurately describe how the platform works under the hood.Originality: The content must be 100% original, well-structured, and written in a professional, engaging tech-blog tone.Scope of WorkYou will be provided with:Full access to the working Streamlit application code.Complete backtesting outputs, performance metrics, rebalancing comparisons, and ML diagnostics (OOS Pearson correlation, $R^2$, feature importance).Performance charts, stress test results (COVID-19, 2022 bear market), and risk tables generated by the app.Proposed Article StructureIntroduction & Overview: The problem with traditional portfolio optimization and how machine learning bridges the gap.The Framework (Tech Stack & Methodology): A deep dive into the data pipeline, the traditional models used, and how the ML models (LightGBM, Random Forest, etc.) handle walk-forward validation.Backtesting & Rebalancing Analysis: Explaining the impact of different rebalancing frequencies (weekly, monthly, quarterly) on performance.Results & Performance Breakdown: Presenting and explaining the data tables, model correlation heatmaps, and how the strategies performed during market stress tests.Key Takeaways & Future Roadmap: Summary of findings, practical limitations in live trading, and next steps for the platform.Timeline & CompensationTimeline: First draft within 14 days; final polish within 25 days.Budget: Competitive fixed rate or per-word rate based on experience. Payment will be split into milestones (e.g., 40% on first draft, 60% on final approval).How to ApplyPlease reply with:A brief summary of your experience writing about quantitative finance or machine learning.2–3 relevant writing samples (Medium articles, whitepapers, or GitHub documentation).Your proposed rate for a project of this scope.To confirm you've read the full description, please start your reply with the word Portfolio i hope to find someone who matches my reuirements

Contract | Remote | 6 Months | Full-time, 40 hrs/week
Skills: Python | React | LangChain

***YOU MUST SUBMIT YOUR RESUME IN MS WORD FORMAT TO BE CONSIDERED FOR THE ROLE***

OVERVIEW
Our client is a scientific data science company building an Agentic Curation Platform — a web application that uses AI agents to automate biomedical data curation at scale. As the Senior Full-Stack Engineer, you will own end-to-end platform development including backend services, an AI agent orchestration engine, a complex data editing workspace, validation framework, and containerized deployment. You will work directly alongside the client's internal AI and data science team in a fast iterative model with daily standups and weekly demos.
This is not a standard web application. It is a specialized AI-powered data editing environment with significant backend complexity. We need a senior engineer who has built systems like this before.

WHAT YOU'LL BUILD
AI Agent Orchestration Engine — execution runtime managing hierarchical AI skill invocation, Claude API (Anthropic) integration, token limits, retries, cost tracking, and full execution logging
Data Ingestion Pipeline — parsing and indexing CSVs, TSVs, Excel files, PDFs, and structured tabular data files
Tabular Curator Workspace — complex React data editing interface with paired source/value columns, attribution popovers, column preset management, entity navigation, and inline validation
Batch Operations Framework — pluggable operation catalog, configuration panels, backend execution, and a preview overlay with cell-level accept/reject controls
Validation Engine — continuous hard validation covering field types, controlled vocabularies, ontology terms, cross-field consistency, and cross-entity referential integrity
Project Management Dashboard — project groups, dataset status workflow, curator assignment, and workload views
Export Layer — CSV and XLSX output by entity
Containerized Deployment — Docker-based, deployed to dedicated servers, designed for future on-premise client deployments


TECH STACK:
Backend: Python (FastAPI or equivalent)
Frontend: React
LLM Integration: Claude API / Anthropic SDK, LangChain
Deployment: Docker, dedicated server infrastructure
Database: Open to proposal — must support provenance and audit trail modeling


REQUIRED SKILLS
5+ years Python backend development — REST APIs, async patterns, file processing
5+ years React — complex stateful, data-heavy interfaces
LangChain or equivalent agent orchestration framework in a production system
Experience integrating LLM APIs in production — multi-step pipelines, tool calling, error handling and retry logic
File parsing — PDF, Excel, CSV/TSV including malformed inputs
Docker and dedicated server deployment
Database design with audit trail and provenance modeling
Strong system design skills — you are building from scratch


Working Hours & Availability
Open to Offshore candidates. You must be available for a minimum of 4 to 6 hours of overlap with US Pacific Standard Time (PST) daily, Monday through Friday. Full-time commitment of 40 hours per week, 8 hours per day is required. You must be reachable and responsive during your overlap window for daily standups, demos, and real-time collaboration with the client team.

***YOU MUST SUBMIT YOUR RESUME IN MS WORD FORMAT TO BE CONSIDERED FOR THE ROLE***

• Answer each explicitly. Years of experience in (1 Full-Stack dev (2) React (3) Python (4) Python back-end (5) LangChain or equivalent orchestration agent framework (6) LLM API integration into Prod (7) System design?
• (1) Can you work Mon - Fri, 8 hours/day, 5 days/week, 40 hours/week? (2) How many hours can you overlap & be available with US Pacific time? assuming work starts 7am PST?
• Rate your communication for (1) written (2) spoken skills from 1 - 5, with 5 being the best.
• Have you uploaded your resume in MS Word? Otherwise, you will not be considered

I am competing in a HackerEarth data-science contest and I want to push my score into the top tier of the leaderboard before the challenge closes next month. I have already set up the competition workspace and can grant you immediate access to the dataset, rules, and current public-leaderboard baseline.

Here is what I need from you: build, tune, and iterate a high-performing regression model, supply well-commented code (Python is preferred, but you are free to choose the stack you are fastest with), and hand over a prediction file ready for final submission. Along the way, please keep a concise notebook or script that allows me to reproduce your workflow end-to-end on my own machine.

In your bid, include links or brief summaries of past work that demonstrate strong results in similar competitions or real-world regression problems—this will help me gauge fit quickly.

Key deliverables:
• Reproducible code/notebook with clear comments
• Final ranked prediction file for submission
• Short read-me explaining feature engineering and hyper-parameter choices

The timeline is firm: everything must be wrapped up within one month, ideally with enough buffer to test final tweaks before the deadline. I’ll be available daily for quick questions and can provide rapid feedback on interim submissions. If you thrive on leaderboard pressure and have the portfolio to prove it, I’m looking forward to working together.

Skills: Python, Statistics, Machine Learning (ML), Statistical Analysis, Data Science, Data Analysis, Data Modeling, Predictive Analytics, Model Tuning, Regression Analysis

We are looking for an AI Engineer with experience in Machine Learning, Deep Learning, and Computer Vision to build intelligent and scalable AI solutions. The ideal candidate should have strong skills in Python, AI model development, and automation workflows. Experience with TensorFlow, PyTorch, OpenCV, and real world AI applications is preferred. This role is ideal for someone passionate about developing high performance AI systems and innovative solutions.

We’re looking for experienced AI professionals to provide short, original quotes, practical insights, and light content feedback for our educational articles and guides.

Your real-world perspective will help make the content more accurate, useful, and trustworthy for readers.

The initial project involves reviewing and contributing to one guide, with the possibility of ongoing work.

Example guide: onlinemastersdegrees.org/best-programs/information-systems/

**What You’ll Do:**

* Review AI education content for accuracy and clarity
* Leave light feedback through Google Docs comments
* Provide brief expert quotes, usually 2–5 sentences each
* Offer practical insights based on real-world AI, machine learning, or data science experience
* Help add context around AI careers, degree programs, certifications, skills, tools, and industry expectations

**For the Initial Project:**

We’re looking to add approximately 3–4 short expert quotes to one AI guide. Quotes should be original, practical, and based on your professional experience.

**Details:**

* $30 per page
* Pages typically take 20–30 minutes
* Clear guidelines and examples provided
* Contract, flexible, and ongoing work

**Relevant Experience May Include:**

* Artificial intelligence
* Machine learning
* Data science
* Generative AI
* Natural language processing
* Computer vision
* AI product development
* MLOps
* AI governance, risk, or compliance
* Responsible AI
* AI education or workforce development

**In your submission, please include:**

1. A few sentences about your AI background, professional experience, and areas of expertise
2. Any relevant degrees, certifications, credentials, or notable AI projects
3. Link to your LinkedIn profile

To help us sort through automated submissions, please put the name of Shopify’s CEO at the top of your submission.

**Job Description:**

We are seeking a highly skilled and experienced freelancer to develop a robust and sophisticated credit underwriting model for our small finance company based in India. Our organization is dedicated to providing accessible financial solutions to our clients, and we recognize the critical importance of effective credit risk assessment in achieving this goal. Therefore, we are looking for a candidate who possesses not only technical expertise but also a deep understanding of the financial landscape.

The ideal candidate will have an extensive background in both financial modeling and machine learning, with a proven track record of successfully analyzing credit data and developing predictive models that drive informed decision-making. You should be adept at leveraging advanced statistical techniques and algorithms to create models that can accurately assess credit risk, thereby enabling us to make sound lending decisions and manage our portfolio effectively.

Key responsibilities of this role include, but are not limited to:

1. **Data Collection and Preparation:** Gather, clean, and preprocess relevant credit data from various sources to ensure the accuracy and reliability of the information used in the modeling process. This may involve working with both structured and unstructured data.

2. **Model Development:** Design and build a comprehensive credit underwriting model that incorporates various factors influencing creditworthiness, including but not limited to credit scores, income levels, employment history, and economic indicators.

3. **Model Validation and Testing:** Rigorously test and validate the model to ensure its reliability and predictive power. This will involve conducting back-testing and sensitivity analysis to assess performance under different scenarios.

4. **Risk Assessment:** Develop methodologies for assessing the risk associated with loan applications and provide actionable recommendations for loan approvals based on the model outcomes.

5. **Collaboration and Communication:** Work closely with our finance team to understand their needs and incorporate their insights into the model. Additionally, you will be responsible for presenting findings and recommendations to stakeholders in a clear and concise manner.

6. **Continuous Improvement:** Stay abreast of the latest trends and advancements in financial modeling and machine learning. Continuously seek opportunities to enhance the model's accuracy and efficiency based on feedback and real-world performance.

7. **Documentation:** Create comprehensive documentation that outlines the modeling process, assumptions made, and the rationale behind key decisions. This documentation will be vital for future reference and for onboarding team members.

The ideal candidate will possess a master's degree in finance, statistics, data science, or a related field, along with several years of relevant experience in credit risk modeling and analysis. A strong proficiency in programming languages such as Python or R, as well as experience with machine learning libraries and tools, is essential. Additionally, you should demonstrate a passion for financial analysis, a keen eye for detail, and the ability to work independently while managing multiple tasks effectively.

If you are excited about the opportunity to make a meaningful impact in the finance sector and have the skills and experience we are looking for, we would love to hear from you. Please submit your resume, along with a cover letter detailing your relevant experience and any previous projects that showcase your expertise in credit underwriting and predictive modeling. Join us in our mission to empower individuals and businesses through smart and responsible lending practices.

I'm looking for an experienced data science professional who can provide high-quality, professional-level data science projects to strengthen and enhance our GitHub portfolio.
The main goal is to build a strong and credible GitHub presence that reflects real-world data science experience and showcases the profile of a professional Data Scientist.

Responsibilities:
- Provide complete and well-structured data science projects
- Deliver clean, production-quality code and documentation
- Create professional README files with:
- Project overview
- Business/problem statement
- Dataset explanation
- Methodology and model approach
- Results and insights
- Installation and usage instructions
- Organize repositories professionally for portfolio presentation
- Maintain realistic project structure and development workflow

Required Skills:
- Strong background in Data Science and Machine Learning
- Experience with Python, Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch
- Experience with data visualization and analytics
- Strong Git and GitHub knowledge
- Ability to write professional technical documentation

Preferred:
- Experience creating portfolio-ready GitHub repositories
- Knowledge of end-to-end ML workflows
- Experience with real-world datasets and business use cases

• Describe your recent experience with similar projects

I need someone to extract customer and second-level data from RepairShopr and put it into a CSV file. The task involves handling tax codes as well. The ideal candidate should have experience in data extraction and management, particularly with software like RepairShopr.

Job Title: AI Tools Specialist (ChatGPT, Claude, Gemini)

We are looking for a skilled freelancer who has experience working with AI tools such as ChatGPT, Claude, and Gemini.

Responsibilities:
Use AI tools to generate high-quality content and responses
Assist with data entry, research, and writing tasks
Create accurate, well-structured outputs based on instructions
Support daily operational tasks using AI tools
Requirements:
Experience with ChatGPT, Claude, or Gemini
Good English communication skills
Ability to follow instructions carefully
Detail-oriented and fast learner
Work Type:
Remote / Freelance
Part-time or full-time available

AI Talent Evaluation Consultant (Part-Time, Hourly Contract)

Role Overview:
We are seeking a part-time, hourly AI Talent Evaluation Consultant to support recruitment teams by rigorously assessing whether candidates can substantiate and demonstrate the AI capabilities they claim. This role is focused on validating real-world competence—ensuring that candidates not only understand AI concepts but can effectively apply them in practice. The consultant will design screening frameworks, test practical knowledge, and conduct in-depth interviews to verify alignment with job requirements.

Key Responsibilities:
• Partner with recruiters and hiring managers to understand AI requirements across multiple roles.
• Critically evaluate candidate resumes, portfolios, and project descriptions to distinguish between theoretical knowledge and applied experience.
• Design targeted screening questions that test practical AI capability, problem-solving, and depth of understanding.
• Create validation frameworks to help recruiters assess whether candidates can genuinely perform the tasks they claim.
• Conduct technical interviews focused on real-world application, including probing past project contributions and decision-making.
• Challenge and verify candidate claims through scenario-based questioning, case discussions, or practical exercises where appropriate.
• Provide clear, evidence-based feedback highlighting strengths, gaps, and any inconsistencies in candidate claims.
• Continuously refine evaluation methods to improve accuracy in identifying true AI capability.
Required Qualifications:
• Proven hands-on experience in AI, machine learning, data science, or a related field.
• Strong ability to assess practical application of AI concepts beyond theoretical knowledge.
• Experience interviewing, mentoring, or technically evaluating candidates or peers.
• Ability to identify inconsistencies or exaggerations in technical claims through structured questioning.
• Excellent communication skills, with the ability to translate technical assessments into clear hiring recommendations.

Preferred Qualifications:
• Experience designing technical assessments, case studies, or interview frameworks.
• Exposure to multiple AI domains (e.g., NLP, computer vision, generative AI, MLOps).
• Consulting or advisory experience across different teams or projects.

Key Competencies:
• Critical and investigative thinking
• Strong judgment and attention to detail
• Ability to validate practical competence vs. theoretical knowledge
• Clear and objective feedback delivery

Engagement Details:
• Part-time, hourly contract role
• Flexible working hours based on hiring needs
• Project-based or ongoing support depending on hiring volume

I'm looking for a GitHub repository setup to showcase my data science projects. The primary purpose of this repo is to serve as a portfolio.

Requirements:
- The repository should include detailed README files with project descriptions.

Ideal Skills and Experience:
- Proficiency in Git and GitHub
- Experience in creating clear and comprehensive README files
- Background in data science projects


I want to buy the access to git hub project I need the repos to be old and have a good commit history

Skills: Data Processing, Project Management, Data Mining, Software Development, Git, Data Science, Data Visualization, Data Analysis, GitHub, Data Management

I am looking for an LLM expert to troubleshoot and optimize an input pipeline. Currently, my prompts are failing to account for the entire input dataset, leading to incomplete or inaccurate analysis.

The ideal candidate has deep experience with:

Context Window Management: Handling large inputs without losing critical data points.

Prompt Engineering: Refining instructions to ensure the model attends to the full context.

Input Pre-processing: Implementing strategies like RAG (Retrieval-Augmented Generation), "Map-Reduce" prompting, or recursive summarization to ensure 100% data consideration.

Goal: Ensure the model processes the full input and provides a synchronized, holistic analysis.

I need a working software application that carries out statistical analysis on research datasets for my final-year project. The focus is purely on stats—no dashboards, mapping, or fancy visualisations—so everything you build should centre on clean numerical insight.

Core functions I must demonstrate:
• Upload or paste a CSV/Excel file.
• Generate descriptive statistics (mean, median, mode, variance, standard deviation).
• Run common inferential tests—t-test, chi-square, ANOVA, basic linear regression—selectable through a simple menu or GUI.
• Export the results as a neatly formatted PDF or Word report so I can attach it to my submission.

I’m flexible on the tech stack: Python (pandas, scipy), Java (Apache Commons-Math), or any language you’re comfortable with, as long as the setup steps are minimal and well-documented. Include the full, commented source code, a short README, and a quick video or screenshots showing the app in use; that will act as proof it meets my college rubric.

Acceptance criteria
1. Application installs and runs on Windows without extra licensing costs.
2. All listed statistical tests work on the sample dataset I’ll provide.
3. Generated report clearly lists methods, test statistics, p-values, and a concise interpretation section.
4. Code quality is good enough that my professor can skim it and understand the workflow.

If you can deliver within two weeks and are comfortable answering the occasional question about the code, let’s get started.

Skills: Java, Python, Statistics, Software Development, Statistical Analysis, Data Science, Data Analysis, Statistical Modeling

AI Research Prompt Engineer / Web Research Specialist

We are looking for detail-oriented freelancers to help create high-quality research prompts for AI systems.

This project involves creating difficult web-based research questions that require deep reasoning, multi-step thinking, and verification across multiple websites. The work is similar to investigative research and AI training combined.

What You Will Do

* Create research prompts/questions that require deep web research
* Verify facts across multiple websites and sources
* Build prompts that have only one correct answer
* Write clear explanations showing how the answer was found
* Evaluate AI-generated responses and identify incorrect answers
* Follow detailed project guidelines and quality standards

Requirements
* Strong internet research and fact-checking skills
* Excellent attention to detail
* Strong written English
* Ability to follow complex instructions carefully
* Critical thinking and reasoning skills
* Comfortable working with AI tools like ChatGPT, Claude, Gemini, etc.

Preferred Experience
* Prompt engineering
* AI training or RLHF projects
* Research analysis
* Technical writing
* Academic research
* OSINT or investigative research
* Data annotation or AI evaluation projects

Important
This is NOT simple content writing or blog writing. The work requires deep research, logical reasoning, and accuracy.

Please include the following in your proposal:

1. A short summary of your research experience
2. Any AI/prompt engineering experience
3. An example of a difficult research task you completed
4. Why you think you would be a good fit for this project

Looking for reliable freelancers who can consistently produce high-quality work.

• Describe your recent experience with similar projects

I’m producing a new video series built around rapid-fire debates between two opposing teams. Each time a speaker makes a claim, the other side can flag it for immediate verification, and that’s where you come in. I need a fact-checker who can work in real time, draw on solid journalistic instincts, and surface reliable, citable information within minutes—sometimes seconds.

Your background should be rooted in Journalism; you know how to separate opinion from evidence, spot misleading framing, and trace statements back to primary data. The conversations will lean heavily into the following:

• Political debates
• Social issues

When a claim is challenged you’ll verify it on the spot, provide a concise verdict, and supply the exact citation so our host can reference it on camera. Speed matters just as much as accuracy, so you should be comfortable juggling multiple browser tabs, databases, and newswires while the discussion rolls on.

Preferred sources & tools:

• Major news outlets for quick, publicly recognizable citations
• AI tools to accelerate searching, summarizing, and cross-checking (I’m happy to experiment with your favorite prompting methods)

The flow on shoot days is straightforward: monitor the discussion through a live feed (or be in-person in Los Angeles), receive a verbal cue when a fact-check is requested, and deliver a confirmation or rebuttal with source links immediately. After each session, I’ll also need a brief log of every claim you verified and the references used so our editors can drop lower-third graphics into the final cut.

If you thrive under time pressure, love digging for the truth, and are eager to blend traditional reporting skills with the latest AI-assisted research tools, let’s talk.

Skills: Research, Statistics, Research Writing, Content Writing, Data Science, Journalism, Data Analysis, Data Collection, AI Assisted Research

Build according to www.aitoolsengine.app (visualization) and as discussed. See attached build plan and timeline.

I would like to wassap to discuss a project for data mining. Are you able to contact me
To connect and discuss

we’re building opinion.trade — a real-time prediction market platform built around narratives, speculation and collective attention.

our core belief is that markets are changing.

in crypto, internet culture and online communities, price is no longer driven purely by fundamentals. narratives move faster than information. attention spreads faster than analysis. communities coordinate around beliefs, uncertainty and social momentum in real time.

people don’t just trade assets anymore.
they trade expectations.
they trade perception.
they trade future attention.

we think prediction markets are becoming the natural interface for that behavior.

not just as gambling platforms or political forecasting tools — but as a live layer for understanding what the internet believes before consensus fully forms.

that’s the direction behind opinion.trade.

we’re interested in:

* how narratives emerge before they become obvious
* how disagreement creates liquidity and engagement
* how online communities coordinate attention
* how speculative behavior spreads across ecosystems
* how probability itself becomes a social signal
* how real-time markets can reflect collective psychology faster than traditional media or research

a large part of the platform revolves around building systems that feel alive:
live market movement, shifting probabilities, event momentum, sentiment dynamics and real-time interaction between people participating in uncertain outcomes.

from a technical perspective, we’re building:

* real-time websocket infrastructure
* scalable event-driven systems
* low-latency market updates
* live probability and pricing systems
* internal analytics and narrative intelligence tooling
* highly responsive frontend interaction layers

but beyond infrastructure, we care deeply about product feel.

we want markets to feel dynamic, reactive and socially alive — more like participating inside a living information system than using a static financial product.

small team, fast iteration, very high ownership environment.

we’re looking for engineers who enjoy difficult product problems, real-time systems and the intersection of markets, psychology, internet culture and human behavior.

people who like building things that probably shouldn’t exist yet — but inevitably will.

Budget: Fixed-price, $500 - or experienced Best Offer

I have a partially built n8n workflow (self-hosted on Windows, running locally) that processes Excel-based work order data through a multi-phase pipeline. The workflow reads from a local .xlsm workbook, applies find/replace normalization rules, then uses a locally hosted Llama model (via Ollama) for AI-assisted categorization and rule diagnostics.
The core logic is roughly 80% complete. I need an experienced n8n developer to finish, stabilize, and optimize the remaining pieces. This is NOT a from-scratch build — you will be working with an existing workflow that has functioning Phase 1 and Phase 2 nodes.
What’s Already Built
• Phase 1: Deterministic find/replace normalization of work order descriptions. Reads local .xlsm via Extract From File nodes, processes through a Code node, outputs cleaned .xlsx. Working.
• Phase 2: AI classification via Llama 3.1 8B (Ollama, local). Batches descriptions, compares Llama’s categories to Excel’s rule-based categories, flags mismatches. Working.
• Phase 3: Rule diagnosis for mismatches — sends mismatches to Llama with trigger/exclude rule context. Partially working but needs refinement.
What Needs to Be Done
• Stabilize the full pipeline end-to-end (Phase 1 → 2 → 3 → 4) with proper Wait node form handling and error recovery
• Build the human approval gate (Phase 4) with batch-approve and individual-approve options
• Write approved rule changes back to the source Excel workbook’s audit tables
• Build a persistent “unknown category” tracking list that accumulates across projects
• Set up a weekly email summary of recommended new categories
• Optimize for scale: the workflow needs to handle 1,500–14,000 rows per run with batched Llama calls
• Add a completion summary at the end of each run
Tech Stack
• n8n (self-hosted, v2.15.1, Windows, npm install)
• Ollama (local) with Llama 3.1 8B and Llama 3.2 3B
• Excel .xlsm workbooks (macro-enabled, complex formula/trigger logic)
• JavaScript Code nodes (not Python)
• Local file I/O via Read/Write Files from Disk + Extract From File nodes (NOT Microsoft Excel 365 cloud nodes)
Required Experience
• Strong n8n workflow development (Code nodes, Merge nodes, branching, Wait/Form nodes, error handling)
• Experience integrating Ollama or other local LLMs with n8n
• JavaScript proficiency (all Code nodes are JS)
• Working with Excel files programmatically (Extract From File, Convert to File, understanding of .xlsm constraints)
• Prompt engineering for structured JSON output from local LLMs
Nice to Have
• Experience with maintenance/facilities management or commercial real estate data
• Familiarity with rule-based categorization systems and how to tune them
To Apply
Please include in your proposal:
• Your experience with n8n (especially self-hosted + Code nodes + LLM integration)
• Whether you’ve worked with Ollama/Llama specifically
• A rough estimate of hours to complete the remaining work
• Any similar projects you’ve done (data pipeline + AI classification + Excel)

A detailed technical brief will be shared with selected candidates via private message.

Estudio ingenieria informatica pero en la carrera no he visto el tema de creacion de agentes agenticos con langchain ni n8n. Busco persona que domine el tema y me enseñe a desarrollarlos y a hacer el deployment de los mismos haciendolos funcionales. No tengo un proyecto especifico, solo deseo aprender a crearlos con todas sus funcionalidades, incluido el manejo de base de datos vectoriales, embeddings, RAG etc.
Gracias a los que puedan responder.

We are seeking a detail-oriented individual to extract research data from two platforms and record it on Excel sheets. The ideal candidate will have experience in data entry and research data management, ensuring accuracy and efficiency in data handling. This role requires basic data analysis skills and the ability to work independently.

Full-Stack AI/ML Developer (ML/ LLMs / Web App MVP) – Immediate Start(More than 5+ years Long-Term Opportunity)

1. Project Overview:
I am managing an active project for a B2B e-learning company in the healthcare space. We are replacing their manual, slow ChatGPT/Claude workflows with a custom, centralized AI web application to automate their educational scriptwriting process.
I am looking to hire a strong Full-Stack AI/ML Developer to act as the hands-on builder for this project.

2.
Phase 1 (Immediate): Finalize the technical architecture and proposal to secure formal client sign-off (we already have the verbal go-ahead).
Phase 2 (6-Week Sprint): Build and deploy the MVP platform from scratch.

3.
What We Are Building (The MVP):
- A lightweight web app with user logins for ~10 scriptwriters.
- Research & Outline Agents: AI pipeline that searches for information, provides source attribution/links to prevent hallucinations, and generates structured course outlines.
- Script Generation: LLM pipeline that takes the approved outline and writes the script.
- "Voice" Matching: The system must use few-shot prompting or RAG to mimic the individual writing style/voice of the specific user logged in based on baseline examples.
- QA Checks: Automated checks to ensure reading level (6th-9th grade) and tone constraints are met.

4.
Required Skills:
Full-Stack Development: React/Next.js (or similar) for a clean, simple UI, and Python/Node.js for the backend.
AI/LLM Integration: Extensive experience with ML, OpenAI API, Anthropic (Claude) API, and prompt engineering.
AI Frameworks: Experience building Agentic workflows and RAG pipelines (LangChain, LlamaIndex, etc.).
Cloud Infrastructure: Deploying and managing web apps on AWS or GCP.
Fast & Agile: Ability to rapid-prototype and iterate based on user feedback.

5.
Working Style & Expectations:**

* Must be available for async communication on Slack during EST hours(8 am ~5 pm EST)
* Must be an independent freelancer (no agencies or teams)
* Strong communication and ownership mindset
* Comfortable working in a fast-moving, startup-like environment

Hourly rate: $25/hr, more than 25 hours per week

To Apply, Please Provide:
A brief summary of a similar LLM-based web app or AI agent workflow you have built.
Your preferred tech stack for building this specific MVP (Frontend, Backend, AI orchestration).
Your availability over the next 6 weeks.
Note: If this MVP is successful, the project will expand to automate translation/localization pipelines and develop video production tools.

xAQUA is an enterprise AI Data Platform — six AI agents on a shared semantic layer, based out of California.
We need senior consultants with expertise in:

Data Engineering
Data Science / ML (XGBoost, MLOps, anomaly detection), Data Semantics, Data Model
GenAI & LLMs (open-source models, agentic workflows, evals)
RAG & GraphRAG (hybrid retrieval, pgvector, knowledge graphs)
Expert Python / FastAPI; enterprise integrations

I need a data-minded strategist who can spot outlier performance in short-form video for the adult space and translate those discoveries into a repeatable, multi-account growth plan. The work goes well beyond “trend surfing”—I’m after someone who can scrape or pull platform data (TikTok, Instagram Reels, YouTube Shorts, and any other avenue you believe can slip past the usual adult-content roadblocks), crunch the numbers, and hand me actionable scripts, hooks, and posting schedules that function as a self-contained ecosystem.

Here’s what the first sprint should cover:

*CAN understand how to use Tiktok creator insights, trends, general app how-tos

• A fresh dataset of top-performing, niche-relevant clips from the past 30–60 days, complete with raw metrics, links, and your notes on why each piece spiked.
• A concise insight deck that breaks down winning hooks, visual patterns, caption structures, and call-to-action styles we can safely adapt to adult audiences.
• A 30-day calendar for at least three coordinated accounts (creator, promo, and aggregator) showing daily topics, cross-posting logic, and timing.
• A light Python / Sheets workflow that lets my internal team refresh the data without bugging you every week.
• A KPI template we can drop straight into Looker Studio or Airtable for weekly reporting.

If you’re comfortable working with public APIs, scraping libraries (BeautifulSoup, Playwright, or similar), and basic data-viz tools, you’ll hit the ground running. Experience inside the adult or influencer marketplace is a big plus, and while I welcome worldwide talent, someone in the DMV area would make quick in-person white-boarding sessions possible.

Kickoff is immediate; I’d like the first round of insights on my desk within 72 hours of contract start, with weekly iterations after that. If this sounds like your wheelhouse, tell me how you’d tackle the initial data pull and any past wins you can share.

Skills: Video Services, Web Scraping, Social Media Marketing, Instagram, Data Science, Data Scraping, Data Visualization, Data Analysis, TikTok, Data Management

We are seeking a detail-oriented individual to extract research data from two platforms and record it on Excel sheets. The ideal candidate will have experience in data entry and research data management, ensuring accuracy and efficiency in data handling. This role requires basic data analysis skills and the ability to work independently.

We are seeking a lawyer with expertise in SaaS privacy and data protection to draft and review legal documents. Responsibilities include ensuring compliance with CCPA and GDPR, reviewing app terms, and creating software agreements. The ideal candidate will have experience with technology contracts and subscription agreements.

I am looking for an experienced Quantitative Developer / Systems Architect to design and deploy a robust, low-latency algorithmic trading platform. The goal is to move from manual execution to a fully automated system that handles data ingestion, strategy execution, and risk management.

Scope of Work
Infrastructure Setup: Configure a secure, Homelab / cloud-based environment (Oracle) optimized for 24/7 uptime.

Exchange Integration: Connect via REST/WebSocket APIs to CMC Market, Interactive Brokers, Td waterhouse, Questrade.

Strategy Implementation: Translate my existing logic into clean, backtested code. The strategy involves Mean Reversion, Momentum, Arbitrage or others .

Risk Management Engine: Develop "Circuit Breakers" including Hard Stop Losses, Max Daily Drawdown limits, and position sizing logic.

Dashboard/Logging: Set up a basic UI or Telegram/Discord bot for real-time trade alerts and performance tracking.

Technical Requirements
Languages: Proficiency in Python (Pandas, NumPy, TA-Lib) or C++.

Frameworks: Experience with Backtrader, Lean (QuantConnect), or CCXT.

DevOps: Docker, Linux server management, and basic cybersecurity protocols.

Database: Experience with Time-Series Databases (InfluxDB, Kdb+) is a plus.

Preferred Qualifications
Proven track record of building "production-ready" bots (not just backtesting scripts).

Deep understanding of slippage, latency, and order book dynamics.

Ability to explain complex logic simply and provide clean, documented code.

Application Questions
Which exchanges/brokers have you successfully integrated with using Python?

How do you handle API disconnections or "flash crash" scenarios in your code?

Can you provide a brief overview of a previous trading system you built and the tech stack used?

What is your preferred method for backtesting to ensure "Look-Ahead Bias" is avoided?